"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

__SCREAMING_SNAKE_CASE             =logging.get_logger(__name__)



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[Any] ):
     if isinstance(__SCREAMING_SNAKE_CASE	,     (list, tuple) ) and isinstance(videos[0]	,     (list, tuple) ) and is_valid_image(videos[0][0] ):
          return videos

     elif isinstance(__SCREAMING_SNAKE_CASE	,     (list, tuple) ) and is_valid_image(videos[0] ):
          return [videos]

     elif is_valid_image(__SCREAMING_SNAKE_CASE ):
          return [[videos]]

     raise ValueError(F'''Could not make batched video from {videos}''' )







class 			UpperCamelCase						(     lowercase_    ):
   lowercase				=    ['pixel_values']
   def __init__(     self	,__UpperCamelCase = True	,__UpperCamelCase = None	,__UpperCamelCase = PILImageResampling.BILINEAR	,__UpperCamelCase = True	,__UpperCamelCase = None	,__UpperCamelCase = True	,__UpperCamelCase = 1 / 255	,__UpperCamelCase = True	,__UpperCamelCase = True	,__UpperCamelCase = None	,__UpperCamelCase = None	,**__UpperCamelCase	,)      -> None:


        '''simple docstring'''


        super().__init__(**_A     )
        lowercase_      :							Dict						=      size if size is not None else {'''shortest_edge''': 256}
        lowercase_      :							Dict						=      get_size_dict(_A	,default_to_square=_A     )
        lowercase_      :							List[Any]						=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        lowercase_      :							Dict						=      get_size_dict(_A	,param_name='crop_size'     )

        lowercase_      :							Union[str, Any]						=      do_resize
        lowercase_      :							Union[str, Any]						=      size
        lowercase_      :							Optional[Any]						=      do_center_crop
        lowercase_      :							Dict						=      crop_size
        lowercase_      :							Any						=      resample
        lowercase_      :							Optional[int]						=      do_rescale
        lowercase_      :							Dict						=      rescale_factor
        lowercase_      :							Tuple						=      offset
        lowercase_      :							Dict						=      do_normalize
        lowercase_      :							int						=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowercase_      :							List[str]						=      image_std if image_std is not None else IMAGENET_STANDARD_STD
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase = PILImageResampling.BILINEAR	,__UpperCamelCase = None	,**__UpperCamelCase	,)      -> np.ndarray:


        '''simple docstring'''


        lowercase_      :							Dict						=      get_size_dict(_A	,default_to_square=_A     )
        if "shortest_edge" in size:
             lowercase_      :							List[Any]						=      get_resize_output_image_size(_A	,size['shortest_edge']	,default_to_square=_A     )
        elif "height" in size and "width" in size:
             lowercase_      :							str						=      (size['''height'''], size['''width'''])
        else:
             raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''     )
        return resize(_A	,size=_A	,resample=_A	,data_format=_A	,**_A     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase = None	,**__UpperCamelCase	,)      -> np.ndarray:


        '''simple docstring'''


        lowercase_      :							int						=      get_size_dict(_A     )
        if "height" not in size or "width" not in size:
             raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''     )
        return center_crop(_A	,size=(size['height'], size['width'])	,data_format=_A	,**_A     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase = True	,__UpperCamelCase = None	,**__UpperCamelCase	,)      -> Tuple:


        '''simple docstring'''


        lowercase_      :							str						=      image.astype(np.floataa     )
        if offset:
             lowercase_      :							Optional[int]						=      image - (scale / 2)
        return rescale(_A	,scale=_A	,data_format=_A	,**_A     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase = None	,**__UpperCamelCase	,)      -> np.ndarray:


        '''simple docstring'''


        return normalize(_A	,mean=_A	,std=_A	,data_format=_A	,**_A     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = ChannelDimension.FIRST	,)      -> np.ndarray:


        '''simple docstring'''


        if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'     )

        if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'     )

        if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'     )

        if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'     )

        if offset and not do_rescale:
             raise ValueError('For offset, do_rescale must also be set to True.'     )

        # All transformations expect numpy arrays.
        lowercase_      :							List[Any]						=      to_numpy_array(_A     )

        if do_resize:
             lowercase_      :							Optional[Any]						=      self.resize(image=_A	,size=_A	,resample=_A     )

        if do_center_crop:
             lowercase_      :							int						=      self.center_crop(_A	,size=_A     )

        if do_rescale:
             lowercase_      :							Any						=      self.rescale(image=_A	,scale=_A	,offset=_A     )

        if do_normalize:
             lowercase_      :							Dict						=      self.normalize(image=_A	,mean=_A	,std=_A     )

        lowercase_      :							Optional[Any]						=      to_channel_dimension_format(_A	,_A     )
        return image







   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = None	,__UpperCamelCase = ChannelDimension.FIRST	,**__UpperCamelCase	,)      -> PIL.Image.Image:


        '''simple docstring'''


        lowercase_      :							List[Any]						=      do_resize if do_resize is not None else self.do_resize
        lowercase_      :							List[Any]						=      resample if resample is not None else self.resample
        lowercase_      :							Optional[int]						=      do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_      :							Dict						=      do_rescale if do_rescale is not None else self.do_rescale
        lowercase_      :							Optional[Any]						=      rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_      :							Union[str, Any]						=      offset if offset is not None else self.offset
        lowercase_      :							int						=      do_normalize if do_normalize is not None else self.do_normalize
        lowercase_      :							Optional[int]						=      image_mean if image_mean is not None else self.image_mean
        lowercase_      :							Tuple						=      image_std if image_std is not None else self.image_std

        lowercase_      :							Dict						=      size if size is not None else self.size
        lowercase_      :							List[Any]						=      get_size_dict(_A	,default_to_square=_A     )
        lowercase_      :							List[Any]						=      crop_size if crop_size is not None else self.crop_size
        lowercase_      :							Optional[Any]						=      get_size_dict(_A	,param_name='crop_size'     )

        if not valid_images(_A     ):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'     )

        lowercase_      :							Dict						=      make_batched(_A     )

        lowercase_      :							int						=      [
            [
                self._preprocess_image(
                    image=_A	,do_resize=_A	,size=_A	,resample=_A	,do_center_crop=_A	,crop_size=_A	,do_rescale=_A	,rescale_factor=_A	,offset=_A	,do_normalize=_A	,image_mean=_A	,image_std=_A	,data_format=_A	,)
                for img in video
            ]
            for video in videos
        ]

        lowercase_      :							Optional[int]						=      {'''pixel_values''': videos}
        return BatchFeature(data=_A	,tensor_type=_A     )









'''simple docstring'''





from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCamelCase						:						Any  =					tuple[int, int]


class  snake_case__      :




					def __init__(  self  :		List[str]				, _A  :		set[int]				, _A  :		Mapping[EdgeT, int]      )    ->							None:
								UpperCAmelCase_      :       set[int]          =       vertices
								UpperCAmelCase_      :       dict[EdgeT, int]          =       {
								    (min(_A      ), max(_A      )): weight for edge, weight in edges.items()
								}




					def 		A      (  self  :		Union[str, Any]				, _A  :		EdgeT				, _A  :		int      )    ->							None:
								self.vertices.add(edge[0]      )
								self.vertices.add(edge[1]      )
								UpperCAmelCase_      :       List[str]          =       weight





					def 		A      (  self  :		str      )    ->							Graph:
								UpperCAmelCase_      :       Graph          =       Graph({min(self.vertices      )}				, {}      )
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int

								while len(subgraph.vertices      ) < len(self.vertices      ):
											UpperCAmelCase_      :       Optional[Any]          =       max(self.edges.values()      ) + 1
											for edge, weight in self.edges.items():
														if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																	if weight < min_weight:
																				UpperCAmelCase_      :       Dict          =       edge
																				UpperCAmelCase_      :       Tuple          =       weight

											subgraph.add_edge(_A				, _A      )

								return subgraph






def __UpperCAmelCase   (					A					:			str = "p107_network.txt"		)    ->   int:
			UpperCAmelCase_      :       str          =       os.path.abspath(os.path.dirname(A		)		)
			UpperCAmelCase_      :       str          =       os.path.join(A			,       A		)
			UpperCAmelCase_      :       dict[EdgeT, int]          =       {}
			UpperCAmelCase_      :       list[str]
			UpperCAmelCase_      :       int
			UpperCAmelCase_      :       int

			with open(A		) as f:
						UpperCAmelCase_      :       int          =       f.read().strip().split('''\n'''		)

			UpperCAmelCase_      :       Any          =       [line.split(''','''		) for line in data]

			for edgea in range(1			,       len(A		)		):
						for edgea in range(A		):
									if adjaceny_matrix[edgea][edgea] != "-":
												UpperCAmelCase_      :       Optional[int]          =       int(adjaceny_matrix[edgea][edgea]		)

			UpperCAmelCase_      :       Graph          =       Graph(set(range(len(A		)		)		)			,       A		)

			UpperCAmelCase_      :       Graph          =       graph.prims_algorithm()

			UpperCAmelCase_      :       int          =       sum(graph.edges.values()		)
			UpperCAmelCase_      :       int          =       sum(subgraph.edges.values()		)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')


